Eric Mazumdar - RankMe

74 papers A* 18A 3Journal 39Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization. CoRR Chengrui Qu, Christopher Yeh, Kishan Panaganti, Eric Mazumdar, Adam Wierman
2026	J	`jnl`	Provably Convergent Actor-Critic in Risk-averse MARL. CoRR Yizhou Zhang, Eric Mazumdar
2026	J	`jnl`	Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation. CoRR Jake Gonzales, Max Horwitz, Eric Mazumdar, Lillian J. Ratliff
2026	J	`jnl`	Training Generalizable Collaborative Agents via Strategic Risk Aversion. CoRR Chengrui Qu, Yizhou Zhang, Nicolas Lanzetti, Eric Mazumdar
2025	A*	`conf`	Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning. ICML Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman
2025	—	`conf`	Computing Optimal Transport Plans via Min-Max Gradient Flows. CDC Lauren E. Conger, Franca Hoffmann, Ricardo Baptista, Eric Mazumdar
2025	—	`conf`	Convergent Q-Learning for Infinite-Horizon General-Sum Markov Games through Behavioral Economics. CDC Yizhou Zhang, Eric Mazumdar
2025	J	`jnl`	Convergent Q-Learning for Infinite-Horizon General-Sum Markov Games through Behavioral Economics. CoRR Yizhou Zhang, Eric Mazumdar
2025	A*	`conf`	Learning to Steer Learners in Games. ICML Yizhou Zhang, Yian Ma, Eric Mazumdar
2025	J	`jnl`	Learning to Steer Learners in Games. CoRR Yizhou Zhang, Yi-An Ma, Eric Mazumdar
2025	—	`conf`	Plan for the Worst with Advice: Advice-augmented Robust Markov Decision Processes. CDC Tinashe Handina, Kishan Panaganti, Eric Mazumdar, Adam Wierman
2025	A*	`conf`	Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning. ICLR Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas J. Spanos
2025	J	`jnl`	Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning. CoRR Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas J. Spanos
2025	A*	`conf`	Tractable Multi-Agent Reinforcement Learning through Behavioral Economics. ICLR Eric Mazumdar, Kishan Panaganti, Laixi Shi
2024	J	`jnl`	Can We Break the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning? CoRR Laixi Shi, Jingchu Gai, Eric Mazumdar, Yuejie Chi, Adam Wierman
2024	—	`conf`	Characterizing Controllability and Observability for Systems with Locality, Communication, and Actuation Constraints. CDC Lauren E. Conger, Yiheng Lin, Adam Wierman, Eric Mazumdar
2024	—	`conf`	Follower Agnostic Learning in Stackelberg Games. CDC Chinmay Maheshwari, James Cheng, Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar
2024	A*	`conf`	Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities. NeurIPS Zaiwei Chen, Eric Mazumdar
2024	J	`jnl`	Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
2024	J	`jnl`	Model-Free Robust φ-Divergence Reinforcement Learning Using Both Offline and Online Data. CoRR Kishan Panaganti, Adam Wierman, Eric Mazumdar
2024	A*	`conf`	Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data. ICML Kishan Panaganti, Adam Wierman, Eric Mazumdar
2024	A*	`conf`	Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms. NeurIPS Pierre Clavier, Laixi Shi, Erwan Le Pennec, Eric Mazumdar, Adam Wierman, Matthieu Geist
2024	J	`jnl`	Rethinking Scaling Laws for Learning in Strategic Environments. CoRR Tinashe Handina, Eric Mazumdar
2024	A*	`conf`	Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. ICML Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
2024	J	`jnl`	Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty. CoRR Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
2024	J	`jnl`	Tractable Equilibrium Computation in Markov Games through Risk Aversion. CoRR Eric Mazumdar, Kishan Panaganti, Laixi Shi
2024	A*	`conf`	Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. EC Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
2024	A*	`conf`	Understanding Model Selection for Learning in Strategic Environments. NeurIPS Tinashe Handina, Eric Mazumdar
2023	A*	`conf`	A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. NeurIPS Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
2023	J	`jnl`	A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games. CoRR Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
2023	A*	`conf`	Algorithmic Collective Action in Machine Learning. ICML Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic
2023	J	`jnl`	Algorithmic Collective Action in Machine Learning. CoRR Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic
2023	J	`jnl`	Convergent First-Order Methods for Bi-level Optimization and Stackelberg Games. CoRR Chinmay Maheshwari, S. Shankar Sastry, Lillian J. Ratliff, Eric Mazumdar
2023	J	`jnl`	Coupled Gradient Flows for Strategic Non-Local Distribution Shift. CoRR Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff
2023	—	`conf`	Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees. L4DC Lauren E. Conger, Sydney Vernon, Eric Mazumdar
2023	A*	`conf`	Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows. NeurIPS Lauren E. Conger, Franca Hoffmann, Eric Mazumdar, Lillian J. Ratliff
2023	J	`jnl`	Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games. CoRR Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman E. Ozdaglar, Adam Wierman
2022	J	`jnl`	A Note on Zeroth-Order Optimization on the Simplex. CoRR Tijana Zrnic, Eric Mazumdar
2022	A*	`conf`	Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. NeurIPS Chinmay Maheshwari, Shankar Sastry, Eric Mazumdar
2022	J	`jnl`	Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets. CoRR Chinmay Maheshwari, Eric Mazumdar, Shankar Sastry
2022	J	`jnl`	Langevin Monte Carlo for Contextual Bandits. CoRR Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar
2022	—	`conf`	Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC Lauren E. Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton
2022	A	`conf`	Zeroth-Order Methods for Convex-Concave Min-max Problems: Applications to Decision-Dependent Risk Minimization. AISTATS Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, Shankar Sastry, Lillian J. Ratliff
2021	J	`jnl`	Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization. CoRR Yaodong Yu, Tianyi Lin, Eric Mazumdar, Michael I. Jordan
2021	A*	`conf`	Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games. NeurIPS Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang
2021	A*	`conf`	Who Leads and Who Follows in Strategic Classification? NeurIPS Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
2021	J	`jnl`	Who Leads and Who Follows in Strategic Classification? CoRR Tijana Zrnic, Eric Mazumdar, S. Shankar Sastry, Michael I. Jordan
2021	J	`jnl`	Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization. CoRR Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar, S. Shankar Sastry, Lillian J. Ratliff
2020	—	`conf`	Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CDC Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
2020	—	`conf`	Expert Selection in High-Dimensional Markov Decision Processes. CDC Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry
2020	J	`jnl`	Expert Selection in High-Dimensional Markov Decision Processes. CoRR Vicenc Rubies-Royo, Eric Mazumdar, Roy Dong, Claire J. Tomlin, S. Shankar Sastry
2020	A*	`conf`	Feedback Linearization for Uncertain Systems via Reinforcement Learning. ICRA Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin
2020	—	`conf`	High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems. CDC Eric Mazumdar, Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry
2020	J	`jnl`	Inverse Risk-Sensitive Reinforcement Learning. IEEE Trans. Autom. Control. Lillian J. Ratliff, Eric Mazumdar
2020	J	`jnl`	Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CoRR Eric Mazumdar, Lillian J. Ratliff
2020	A*	`conf`	On Approximate Thompson Sampling with Langevin Algorithms. ICML Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
2020	J	`jnl`	On Gradient-Based Learning in Continuous Games. SIAM J. Math. Data Sci. Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry
2020	J	`jnl`	On Thompson Sampling with Langevin Algorithms. CoRR Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2020	A	`conf`	Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games. AAMAS Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
2020	J	`jnl`	Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning. CoRR Tyler Westenbroek, Eric Mazumdar, David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry
2019	A	`conf`	Convergence Analysis of Gradient-Based Learning in Continuous Games. UAI Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel Burden
2019	J	`jnl`	Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings. CoRR Benjamin Chasnov, Lillian J. Ratliff, Eric Mazumdar, Samuel A. Burden
2019	J	`jnl`	Feedback Linearization for Unknown Systems via Reinforcement Learning. CoRR Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry, Claire J. Tomlin
2019	—	`conf`	Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games. CDC Eric Mazumdar, Lillian J. Ratliff
2019	J	`jnl`	On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games. CoRR Eric Mazumdar, Michael I. Jordan, S. Shankar Sastry
2019	J	`jnl`	Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings. CoRR Eric Mazumdar, Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry
2018	J	`jnl`	On the Convergence of Competitive, Multi-Agent Gradient-Based Learning. CoRR Eric Mazumdar, Lillian J. Ratliff
2017	J	`jnl`	A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes. CoRR Eric Mazumdar, Roy Dong, Vicenc Rubies-Royo, Claire J. Tomlin, S. Shankar Sastry
2017	—	`conf`	Gradient-based inverse risk-sensitive reinforcement learning. CDC Eric Mazumdar, Lillian J. Ratliff, Tanner Fiez, S. Shankar Sastry
2017	J	`jnl`	Optimal Causal Imputation for Control. CoRR Roy Dong, Eric Mazumdar, S. Shankar Sastry
2017	J	`jnl`	Risk-Sensitive Inverse Reinforcement Learning via Gradient Methods. CoRR Lillian J. Ratliff, Eric Mazumdar
2016	J	`jnl`	To Observe or Not to Observe: Queuing Game Framework for Urban Parking. CoRR Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang
2016	—	`conf`	To observe or not to observe: Queuing game framework for urban parking. CDC Lillian J. Ratliff, Chase Dowling, Eric Mazumdar, Baosen Zhang
2016	—	`conf`	Understanding the impact of parking on urban mobility via routing games on queue-flow networks. CDC Daniel J. Calderone, Eric Mazumdar, Lillian J. Ratliff, S. Shankar Sastry